Electroencephalography (EEG) is a well-established method for assessing brain activity. When measurement electrodes are attached on the skin of the skull surface, the weak biopotential signals generated in brain cortex may be recorded and analyzed. The EEG has been in wide use for decades in basic research of the neural systems of the brain as well as in the clinical diagnosis of various central nervous system diseases and disorders.
The EEG signal represents the sum of excitatory and inhibitory potentials of large numbers of cortical pyramidal neurons, which are organized in columns. Each EEG electrode senses the average activity of several thousands of cortical pyramidal neurons.
The EEG signal is often divided into four different frequency bands: Delta (0.5-3.5 Hz), Theta (3.5-7.0 Hz), Alpha (7.0-13.0 Hz), and Beta (13.0-32.0 Hz). In an adult, Alpha waves are found during periods of wakefulness, and they may disappear entirely during sleep. Beta waves are recorded during periods of intense activation of the central nervous system. The lower frequency Theta and Delta waves reflect drowsiness and periods of deep sleep.
Different derangements of internal system homeostasis disturb the environment in which the brain operates, and therefore the function of the brain and the resulting EEG are disturbed. The EEG signal is a very sensitive measure of these neuronal derangements, which might be reflected in the EEG signal either as changes in membrane potentials or as changes in synaptic transmission. A change in synaptic transmission occurs whenever there is an imbalance between consumption and supply of energy in the brain. This means that the EEG signal serves as an early warning of a developing injury in the brain.
According to the present state of knowledge, the EEG signal is regarded as an effective tool for monitoring changes in the cerebral state of a patient. Diagnostically, the EEG is not specific, since many systemic disorders of the brain produce similar EEG manifestations. In Intensive Care Units, an EEG signal may be of critical value, as it may differentiate between broad categories of psychogenic, epileptic, metabolic-toxic, encephalopatic and focal conditions.
Epilepsy is the most common neurological disorder, affecting about one percent of the population at some time in their life. One proposed mechanism for the onset of an epileptic seizure is that neurons in a particular region of the brain become synchronized, leading to a reduction of EEG signal complexity in that area. The theory is proved correct by intracranial EEG recordings, cf. McSharry et al.: Comparison of Predictability of Epileptic Seizures by a Linear and Nonlinear Method, IEEE Transactions on Biomedical Engineering, vol. 50, No. 5, May 2003, pp. 628-633. However, when brain activity is recorded from the scalp, the measured signal is a composition originating from multiple sources and methods indicative of the complexity of the signal show an increase during a seizure, cf. U.S. Pat. Nos. 5,743,860 and 5,857,978.
Just as there are numerous seizure types, any type of seizure may manifest as status epilepticus (SE). SE is usually defined as more than 30 minutes of (1) continuous seizure activity, or (2) two or more sequential seizures without full recovery of consciousness between the seizures. Status epilepticus is often divided into convulsive and nonconvulsive types. The EEG, which demonstrates ongoing ictal activity, can be used to further subdivide SE into either generalized (abnormal activity in the whole brain) or partial SE (abnormal activity in a particular region of the brain). Convulsive status epilepticus (CSE) is the most serious, frequent, and most easily recognized type of SE. It may occur either in primary generalized epilepsy or be secondarily generalized. It is characterized by loss of consciousness and recurrent or continuous convulsions. CSE is a medical emergency and is associated with high morbidity and mortality. Nonconvulsive status epilepticus is often defined as an epileptic state of more than 30 minutes with some clinically evident change in mental status or behaviour from baseline and ictal activity in the EEG.
In status epilepticus, the epileptiform spikes typically last only for a fraction of seconds, but the use of the EEG leans towards the fact that by using long lasting recordings, the EEG signal can reflect slow trend changes. Also, if a seizure occurs during measurements, the EEG signal helps to categorize the epileptiform patterns and seizure activity as a specific type of epilepsy, as well as identify the non-convulsive forms of status epilepticus. In addition, the EEG signal may be used as a control tool for inducing a barbiturate sleep to a level where there are no visible seizures.
Encephalopathy commonly refers to central nervous system dysfunction of any cause, and it can be classified further as either an epileptic encephalopathy or epileptiform encephalopathy. While epileptic encephalopathies are characterized by frequent seizures, epileptiform encephalopathies refer to disorders with epileptiform activity without marked clinical seizure activity. As mentioned above, epileptiform activity commonly refers to signal waveforms or patterns which are typical in epilepsy and which may also be associated with an increased risk of seizures. However, due to the relationship between epilepsy and encephalopathy, similar waveforms or patterns may also appear in other states than in epilepsy, such as in encephalopathy. It is also to be noted in this context that detected epileptiform activity does not alone confirm a diagnosis, but the patient needs to be further examined.
Most of the metabolic and systemic disorders have EEG correlates, and if there is a disturbance of conscious level, the EEG is never normal. However, the EEG findings in encephalopathy have many similarities to those during sedation and anesthesia, which makes the detection of encephalopathy in sedated patients difficult. Generally, when a patient loses consciousness, a shift of spectral power towards lower frequencies appears. Generalized slowing apply also in the case of encephalopathy, however additive periodical and miscellaneous patters often appear in the EEG. Periodical patterns can be, for example, periodic lateralizing epileptiform discharge (PLED) or burst suppression. Miscellaneous patterns are, for example, triphasic waves. Triphasic waves occur about 20-25% of the hepatic encephalopathy patients being, however, not a specific feature for this disease only. They can occur also in other metabolic diseases and noncolvulsive status epilepticus.
In epilepsy, the EEG may include spiky waveforms. While the frequency contents of the spikes may reach up to about 70 Hz, the epileptiform EEG activity is typically below 30 Hz. Periodical patterns of lower frequencies are also typical to epileptiform activity. These patterns include, for example, periodic epileptiform discharges and spike-wave-complexes.
Numerous automatic techniques have been described for the detection and prediction of epileptiform activity. Most of the known methods utilize the whole spectra of an EEG signal. Therefore, the methods are not enough specific to the epileptiform activity only. For example, a spectral entropy has been utilized for investigating the relationships between epileptiform discharges and background EEG activity, cf. T. Inouye et al.: Abnormality of background EEG determined by the entropy of power spectra in epileptic patients, Electroencephalography and clinical Neurophysiology, 82 (1992), pp. 203-207. Epileptiform activity increases the spectral entropy values of the EEG signal data, but the values still remain below the baseline of a conscious patient. The above-mentioned U.S. Pat. Nos. 5,743,860 and 5,857,978 in turn describe analysis methods in which the detection of epilectic seizures is based on non-linear measures of the signal data, such as Kolmogorov entropy. The signal data may be EEG signal data or magnetoencephalographic (MEG) signal data. MEG is indicative of the magnetic component of brain activity, i.e. it is the magnetic counterpart of EEG.
Methods based on wavelet transformation of the EEG signal data have also been proposed for analyzing brain signals, cf. Rosso O A, Blanco S, Yordanova J, Kolev V, Figliola A, Schurmann M, Basar E: Wavelet entropy: a new tool for analysis of short duration brain electrical signals. Journal of Neuroscience Methods 105 (2001), pp. 65-75. In this method, entropy is calculated from the power distribution between the decomposition levels of the transform. In that sense, the technique is thus related to the determination of spectral entropy. However, spectral information is now derived by means of a wavelet transform instead of a Fourier transform.
The article Rosso O A, Blanco S., Rabinowitz A. Wavelet analysis of generalized tonic-clonic epileptic seizures, Signal Processing 2003; 83(6): 1275-1289, describes a wavelet-based method for the analysis of generalized tonic-clonic epileptic seizures. The identification of these seizures is aggravated by the simultaneous muscle activity disturbing the EEG signal. The article describes that wavelet entropy corresponding to a frequency band of 0.8 to 12.8 Hz is lower during seizures than during pre- and post-seizure periods. When a wider frequency band of 0.8 to 51.2 Hz is used, the wavelet entropy first increases at the beginning of seizure, which might be caused by muscle activity.
A further wavelet-based method for analyzing an EEG is described in Geva A B, Kerem D H: Forecasting Generalized Epileptic Seizures from EEG Signal by Wavelet Analysis and Dynamic Unsupervised Fuzzy Clustering, IEEE Transactions on Biomedical Engineering, vol. 45, October 1998, pp. 1205-1216. The method, which is intended for forecasting a generalized epileptic seizure, relies on the existence in the EEG of a preseizure state and utilizes fuzzy clustering for classifying temporal EEG patterns.
One drawback related to the above techniques for automatic detection of epileptiform activity is the weak specificity to epileptiform activity. Therefore, epileptiform activity cannot be distinguished from changes in the level of consciousness of the patient. For example, in the above-described methods based on wavelet entropy the entropy values obtained during an epileptic seizure are typically between the wavelet entropies of the conscious and unconscious states of a patient. Therefore, the methods cannot distinguish, for example, whether an increase in the wavelet entropy is caused by an epileptiform EEG of anesthetized patient or the arousal of the patient.
A further drawback of the prior art detection techniques is that they cannot indicate when a specific type of epileptiform activity is present in the EEG or which type of epileptiform waveforms are present in the EEG signal. Many of the algorithms are also rather complex and require high computation power, which makes them unsuitable for ambulatory devices.
The present invention seeks to eliminate the above-mentioned drawbacks and to bring about a mechanism for detecting epileptiform activity with improved specificity and with the capability to detect specific type of epileptiform signal patterns in the brain wave data obtained from a patient.